The Hierarchical Neuro-Fuzzy BSP Model: An Application in Electric Load Forecasting

Authors:
Flávio J. de Souza;Marley Vellasco;Marco Aurélio Pacheco
Affiliations:
-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Year:
2001

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Abstract

This work presents the development of a novel hybrid system called Hierarchical Neuro-Fuzzy BSP (HNFB) and its application in electric load forecasting. The HNFB system is based on the BSP partitioning (Binary Space Partitioning) of the input space and has been developed in order to bypass the traditional drawbacks of neuro-fuzzy systems: the reduced number of allowed inputs and the poor capacity to create their own structure. To test the HNFB system, we have used monthly load data of six electric energy companies. The results are compared with other forecast methods, such as Neural Networks and Box & Jenkins.